Input assistance device, input assistance method and storage medium

ABSTRACT

To provide an input assistance device, an input assistance method and a storage medium which can present a character string to be suggested to a user in consideration of a probability of being inputted first in a character string. An input assistance device is for assisting a user to input a character string, the device includes: a character string determining unit that determines a character string to be suggested relating to the inputted kana character string, in the case that a user inputs a kana character string by use of index structure, the index structure recording therein a word together with its kana-reading and the index structure indicating whether or not it is probable that the word is inputted first at a time of inputting a character string; and a suggested candidate presenting unit that presents the character string to be suggested, which is determined, as a suggested candidate.

This application is a National Stage Entry of PCT/JP2014/005765 filed onNov. 18, 2014, which claims priority from Japanese Patent Application2013-243128 filed on Nov. 25, 2013, the contents of all of which areincorporated herein by reference, in their entirety.

TECHNICAL FIELD

The present invention relates to an input assistance device, an inputassistance method and a storage medium which, by suggesting anappropriate character string at a time when a user inputs a character,assists the user to input the character.

BACKGROUND ART

Recently, in various kinds of information equipment, a function which,when a user starts inputting a character string into a input form,selects a character string, that matches with the inputted characterstring, out of character strings recorded in advance, and suggests theselected character string as an input candidate is prevailing. Thefunction is generally called ‘auto complete function’ (hereinafter,abbreviated as ‘auto complete’).

As a typical example to which the auto complete is applied, a searchengine is exemplified. In the case of the search engine, as a specificexample, when a user starts inputting a character string into a searchform, the search engine extracts a character string, which matches witha character that the user is inputting, out of search key words whichhas been stored previously, and suggests the extracted character stringto the user Hereinafter, the character string suggested to the user isdescribed as ‘suggested candidate character string’.

Therefore, in the case that the search engine uses the auto complete, itis unnecessary for the user to input a whole of the search key word, andit is possible to input a key word, which the user wants to input, onlyby selecting any one out of the suggested candidate character strings.

Such the auto complete is applicable to not only the search engine butalso every application program which requires inputting into an inputform. As an example of the application other than the search engine, theWeb browser which requires to input URL (Uniform Resource Locator), theelectronic mail software which requires to input a mail address, theelectronic commerce cite which requires to input a product name, or thelike.

Moreover, NPL (non-patent literature) 1 discloses an example of an artthat realizes the auto complete. According to the art which is disclosedby NPL 1, a suggested candidate character string, whose head portion isidentical with a character string inputted by a user, is searched at ahigh speed.

A subject of the art which is disclosed by NPL 1 is language, whosecharacter string inputted by the user with a key board is identical withdescription of the suggested candidate character string, such asEnglish. Moreover, according to the art, the identity judgement on thehead portion is carried out merely by checking whether or not thecharacter cord of the character string inputted by the user is identicalwith the character cord of the suggested candidate character string.Therefore, it is difficult to apply the art, which is disclosed by NPL1, to language, whose character string inputted by the user is notidentical with description of the suggested candidate character string,such as Japanese language.

Specifically, in the case of inputting in the Japanese language, most ofusers input a character string, which the user wants to input, in a kanacharacter by use of the romaji/kana input method or the like, andafterward converts the kana into the kanji (Chinese character).

(Note 1) Japanese language is mainly described by a combination of thekana character and the kanji (Chinese character). The kana is a Japanesesyllabary based on the Chinese character and includes the hiragana andthe katakana. Furthermore, the romaji is alphabetical description of thekana. For example, out of a character string

(inputted character string),

and

are the kanji, and

,

and

are the hiragana. The katakana of

,

and

are

,

and

respectively. Accordingly, since it is conceivable that the inputting isalmost finished at a time when the kana is converted into the kanji, itis too late to suggest the suggested candidate character string to theuser after conversion into the kanji, and it is necessary to suggest thesuggested candidate character string to the user at a time wheninputting the kana.

Therefore, in order to apply the auto complete to the inputting in theJapanese language, it is necessary to beforehand estimate a kana-readingof a character string candidate which is collected from a Japaneselanguage document and to compare a kana character string which isinputted by the user, and the kana-reading of the suggested candidatecharacter string.

(Note 2) The kana-reading indicates how to read the kanji. For example,a kana-reading of

is

in the katakana or

in the hiragana.

As a method for estimating the kana-reading of the suggested candidatecharacter string, a method of using a Japanese language dictionary,which describes relation between the suggested candidate characterstring and its kana-reading, is exemplified. According to the method,when a suggested candidate character string is collected from a Japaneselanguage document, the suggested candidate character string is dividedinto portions each of which is identical with the description whichexists in the dictionary. Next, kana-readings each of which is relatedto each portion are concatenated, and consequently the kana-reading ofthe suggested candidate character string is estimated.

The above-mentioned method for estimating the kana-reading by use of thedictionary can be realized by the method called the morphemic analysis.Furthermore, by use of each kana-reading of each portion of thesuggested candidate character string, an index of the original suggestedcandidate character string is generated, and at a time when a userinputs a kana by use of the index, it is possible to suggest a suggestedcandidate character string which is related to the inputted kana.

Here, an art for suggesting the suggested candidate character string tothe user in reply to the user's inputting the kana will be explained.PTL 1 discloses an art of searching information by use of the voicerecognition.

According to the art which is disclosed by PTL 1, for example, acharacter string of

is divided into four words of

,

,

and

with reference to the dictionary. Here,

,

and

are an abbreviation of

(supermarket), an example of a trade name and an example of a place namerespectively. Then, by using kana-readings of four words which aredescribed in the dictionary, a kana reading of

is estimated.

(Note 3)

is a kana-reading in the katakana of the

which is the kanji.

When the user inputs

with the voice input method, a partial character string of

whose head portion is identical with

is searched, and the original character string of

including the partial character string is estimated and then theoriginal character string is suggested to the user.

CITATION LIST Patent Literature

[PTL 1] WO 2011/030817

Non Patent Literature

[NPL 1] Bo-June (Paul) Hsu and Giuseppe Ottaviano, “Space-Efficient DataStructures for Top-k Completion”, WWW '13 Proceedings of the 22ndinternational conference on World Wide Web, p 583-594, May, 2013

SUMMARY OF INVENTION Technical Problem

As mentioned above, if using the art which is disclosed by PTL 1, it ispossible for the user to be presented with the suggested candidatecharacter string including the kanji only by inputting the kana.However, the art has a problem that accuracy in suggestion is low.

Specifically, in the case that a character string is divided into aplurality of words by merely referring to the dictionary, the acquiredwords include many words which have low probability of being used firstby the user at a time of inputting. Therefore, as a suggestion resultwhich is presented to the user, a character string, which is differentfrom the character string that the user wants to input, is suggested tothe user as the suggested candidate character string, and consequentlythe problem that the accuracy in suggestion is lowered is caused.

For example, in the case that a commodity name of

is recorded, a case that the commodity name is suggested on the basis ofan inputted key word by using the art which is disclosed by PTL 1 willbe studied in the following. In the case that the user inputs

or

,

is usually outputted as the suggested candidate character string.

(Note 4)

means ‘soymilk effective for atopy where

,

’,

and

mean ‘soymilk’, ‘effective’, ‘for’ and ‘atopy’ respectively.Kana-readings of

,

,

and

are

,

and

in the kana respectively.)

However, in the case of using the art which is disclosed by PTL 1, evenwhen the user inputs

, there is a probability that

is outputted as the suggested candidate character string. That is, inthe case of inputting

, it is usually conceived that the user expects words (for example,

: Chrysanthemum and Sword)) other than

. As a result, the user has a suspicious feeling for the suggestion.

Moreover, the reason why the problem that the accuracy in suggestion islow is caused is that the probability of being used first by the user atthe time of inputting is different per the word which exists in thecharacter string. For example,

is a word in front of which

is used. Accordingly, there is a low probability that the user uses

first at the time of inputting with intention of converting

into

, that is, and

is a word which has the low probability of being used first by the userat the time of inputting. Meanwhile,

is apt to be used by itself, and there is a high probability that theuser uses

first at the time of inputting with intention of converting

into

, that is,

is a word which has the high probability of being used first by the userat the time of inputting.

As mentioned above, among the words which are included by the samesuggested candidate character string, there are the word which has thehigh probability of being used first by the user at the time ofinputting, and the word which has the low probability of being used bythe user at the time of inputting. Therefore, the art, which isdisclosed by PTL 1, causes the problem that the accuracy in suggestionis lowered.

An example of an object of the present invention is to solve theabove-mentioned problem, and specifically to provide an input assistancedevice, an input assistance method and a storage medium which canpresent a character string to be suggested to a user in consideration ofa probability of being inputted first in a character string.

Solution to Problem

To achieve the above object, an input assistance device according to oneaspect of the present invention, the device for assisting a user toinput a character string, includes:

a character string determining means that determines a character stringto be suggested relating to the inputted kana character string, in thecase that a user inputs a kana character string by use of indexstructure,

-   -   the index structure recording therein a word together with its        kana-reading and    -   the index structure indicating whether or not it is probable        that the word is inputted first at a time of inputting a        character string; and

a suggested candidate presenting means that presents the characterstring to be suggested, which is determined, as a suggested candidate.

To achieve the above object, an input assistance method according to oneaspect of the present invention is provided for assisting a user toinput a character string. The method includes:

determining a character string to be suggested relating to the inputtedkana character string, in the case that a user inputs a kana characterstring by use of index structure,

-   -   the index structure recording therein a word together with its        kana-reading and    -   the index structure indicating whether or not it is probable        that the word is inputted first at a time of inputting a        character string; and

presenting the character string to be suggested, which is determined, asa suggested candidate.

To achieve the above object, a program stored in storage mediumaccording to one aspect of the present invention is provided assisting auser to input a character string by a computer. The program causes thecomputer implement for:

-   -   determining a character string to be suggested relating to the        inputted kana character string, in the case that a user inputs a        kana character string by use of index structure,        -   the index structure recording therein a word together with            its kana-reading and        -   the index structure indicating whether or not it is probable            that the word is inputted first at a time of inputting a            character string; and

presenting the character string to be suggested, which is determined, asa suggested candidate.

Advantageous Effects of Invention

As mentioned above, according to the present invention, it is possibleto present the character string to be suggested to the user inconsideration of the probability of being inputted first in thecharacter string.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram showing a schematic configuration of an inputassistance device in a first exemplary embodiment of the presentinvention.

FIG. 2 is a block diagram showing a specific configuration of the inputassistance device in the first exemplary embodiment of the presentinvention.

FIG. 3 is a diagram showing an example of information which is recordedin index structure used in the first exemplary embodiment of the presentinvention.

FIG. 4A is a diagram showing an example of a concept on a pointer andits content which are used in the index structure used in the firstexemplary embodiment of the present invention.

FIG. 4B is a conceptual diagram showing an example of the try tree usingthe index structure which is used in the first exemplary embodiment ofthe present invention.

FIG. 5 is a conceptual diagram showing an example of the try tree usingthe index structure which is used in the first exemplary embodiment ofthe present invention.

FIG. 6 is a flow diagram showing an operation of the input assistancedevice in the first exemplary embodiment of the present invention.

FIG. 7 is a block diagram showing a schematic configuration of an inputassistance device in a second exemplary embodiment of the presentinvention.

FIG. 8 is a diagram showing an example of a dictionary which is used forconstructing index structure in the second exemplary embodiment of thepresent invention.

FIG. 9 is a flow diagram showing an operation of the input assistancedevice in the second exemplary embodiment of the present invention.

FIG. 10 is a block diagram showing an example of a computer whichrealizes the input assistance devices in the first and the secondexemplary embodiments of the present invention.

FIG. 11 is a block diagram showing a schematic configuration of an inputassistance device in a third exemplary embodiment of the presentinvention.

DESCRIPTION OF EMBODIMENTS

Hereinafter, an exemplary embodiment of the present invention will beexplained in detail with reference to drawings. In the exemplaryembodiment which will be explained in the following, input assistance iscarried out by use of index structure. But, the index structure in theexemplary embodiment is different from index structure that akana-reading of each word, which is included by a character string to besuggested to a user, is merely recorded. In the exemplary embodiment ofthe present invention, the index structure, in which a word is recordedtogether with its kana-reading and which indicates whether or not it isprobable that, at a time of inputting a character string, each word ofthe character string is inputted first, is used.

Therefore, in the case that the user inputs a kana character which isidentical with a kana-reading of a word having aptness to be inputtedfirst at the time of inputting, a character string including the word issuggested. On the other hand, in the case that the user inputs a kanacharacter which is not identical with the kana-reading of the word,suggestion of a character string is not carried out.

For example, it is assumed that

is recorded as the character string to be suggested, and furthermore

in the katakana)’ and

in the katakana)’ have the probability to be inputted first at the timeof inputting. In this case, when the user inputs

or

,

is suggested. But, when the user inputs

or

,

is not suggested. As a result, the accuracy in suggestion which iscarried out in the input assistance is improved.

Here, as the character string to be suggested, a character string, whichis recorded in advance, is used. Furthermore, a character string, whichis a subject for record, can be acquired from, for example, an inputlog, a product catalog, an address book, or the like which has beenstored by a search system. Moreover, there may be two kinds of ‘word’which is recorded in the index structure, that is, a word which has anunderstandable mean by itself, and a word (incomplete word) which isgenerated by dividing a word which has the understandable mean byitself. As an example of the incomplete word, ‘Af (kana-reading is

)’ and ‘ghan (kana-reading is

) which are acquired by dividing ‘Afghan (kana-reading is

)’ and the like are exemplified.

Moreover, in the exemplary embodiment of the present invention, it is apreferable aspect that, when suggesting a character string, a portion,which is included by the character string of a suggested candidate andwhich is related to a kana character string inputted by the user, ishighlighted and displayed. By virtue of the aspect, the user canunderstand which portion of the character string of the suggestedcandidate the kana character string inputted by the user is related to.Furthermore, the user can quickly judge whether or not the characterstring of the suggested candidate is appropriate.

Furthermore, in the exemplary embodiment of the present invention, theindex structure can be constructed by referring to a dictionary whichdescribes a relation between description of a word and its kana-reading.Specifically, it is assumed that a list of the character strings to besuggested is given in advance. Next, a kana-reading which is related toeach character string is estimated. Moreover, estimation of thekana-reading, which is related to each of the character strings, iscarried out by use of the dictionary. At this time, if a short characterstring, which does not form a word by itself, is recorded by thedictionary, it is possible to improve accuracy in estimating thekana-reading.

Next, each character string which is written in the list is divided intopartial character strings having a short length, and a partial characterstring, which has the probability to be inputted first, is estimated outof the acquired partial character strings. Then, the index structurehaving data structure, in which a kana-reading related to the partialcharacter string having the probability to be inputted first is definedas a key, is constructed.

Moreover, the partial character string is recorded in the indexstructure as ‘word’. Accordingly, the partial character string, which isacquired by the division, may be the word which is understandable byitself, or may be the word (incomplete word) generated by furthermoredividing the word which is understandable by itself. Here, the indexstructure, in which also the incomplete word is recorded, indicateswhether or not it is probable that the word is inputted first. By theabove, a situation in which the incomplete word is suggested by mistakeis avoided.

Moreover, it is possible to judge whether or not it is probable that thepartial character string is inputted first, for example, on the basis ofstatistical data (appearance frequency or the like) on each characterstring existing in a set of documents from which the character string tobe suggested is acquired, or on the basis of a predetermined rule.

(First Exemplary Embodiment)

Hereinafter, an input assistance device, an input assistance method anda storage device in a first exemplary embodiment of the presentinvention will be explained with reference to FIG. 1 to FIG. 6.

[Configuration of Device]

Firstly, a configuration of the input assistance device in the firstexemplary embodiment of the present invention will be explained withreference to FIG. 1. FIG. 1 is a block diagram showing a schematicconfiguration of the input assistance device in the first exemplaryembodiment of the present invention.

An input assistance device 10 in the first exemplary embodiment which isshown in FIG. 1 is a device for assisting a user to input a characterstring. As shown in FIG. 1, the input assistance device 10 includes acharacter string determining unit 11 and a suggested candidatepresenting unit 12.

In the case that the user inputs a kana character string, the characterstring determining unit 11 out of the above-mentioned units determines acharacter string to be suggested, which is related to the inputted kanacharacter string, by use of index structure stored by an index structurestoring unit 13. The index structure records a word and itskana-reading, and indicates whether or not it is probable that each wordis inputted first at a time of inputting a character string. Moreover,the suggested candidate presenting unit 12 presents the character stringto be suggested, which is determined by the character string determiningunit 11, as a suggested candidate.

Moreover, in the first exemplary embodiment, a kana character stringmeans a character string whose conversion into a kanji, a mark, afigure, an alphabet or the like is predetermined. Furthermore, the kanacharacter string may be composed of any one of the hiragana, thekatakana and the romaji. To input a kana character string may be carriedout by directly inputting the kana character or may be carried out byinputting the romaji.

As mentioned above, in the first exemplary embodiment, the indexstructure indicates whether or not it is probable that each word isinputted first by the user. Accordingly, only in the case that a word,which is related to the kana character string inputted by the user, hasthe probability to be inputted first, the input assistance device 10 cansuggest a character string including the word which is related to thekana character string. That is, according to the first exemplaryembodiment, since it is possible to present the suggested candidate tothe user in consideration of the probability of being inputted first inthe character string, differently from the conventional art, it ispossible to avoid a situation of suggesting the unnatural characterstring which is related to the inputted kana character string.

Here, with reference to FIG. 2, the configuration of the inputassistance device 10 will be explained more specifically. FIG. 2 is ablock diagram showing a specific configuration of the input assistancedevice in the first exemplary embodiment of the present invention.

As shown in FIG. 2, according to the present exemplary embodiment, theinput assistance device 10 is realized by a program with using acomputer 20, and assists an input work of the user in various kinds ofapplication programs which are executed by the computer 20. Therefore,the inputting by the user into the input assistance device 10 is carriedout through an input unit 21. The input unit 21 is input equipment,which the computer 20 includes, such as a key board and a touch panel.Moreover, in order to present the suggested candidate to the user, theinput assistance device 10 outputs the character string, which is thesuggested candidate, to a display unit 22. The display unit 22 is adisplay device which the computer 20 includes.

Moreover, as shown in FIG. 2, in the present exemplary embodiment, theinput assistance device 10 includes the above-mentioned index structurestoring unit 13. The index structure storing unit 13 is constructed in astorage area of a storage device (not shown in FIG. 2) such as a harddisk or the like which the computer 20 includes. Here, the indexstructure storing unit 13 may be constructed in a storage area of astorage device which a computer other than the computer 20 includes.

Moreover, according to the present exemplary embodiment, on the basis ofthe index structure which the index structure storing unit 13 stores,the character string determining unit 11 determines that a characterstring, which is related to the inputted kana character string and whichincludes the word having an indication of having the probability ofbeing inputted first at the time of inputting, is the character stringto be suggested.

Specifically, in the present exemplary embodiment, the character stringto be suggested is determined in two cases mentioned in the following.The first case is that, in the index structure which is stored by theindex structure storing unit 13, at least a head portion of akana-reading of a word is identical with the inputted kana characterstring, and the word is recorded. In this case, on the condition that itis indicated that the word has the probability of being inputted firstat the time of inputting, the character string determining unit 11extracts a character string, which includes the word, from the indexstructure storing unit 13 and determines the character string as thesuggested candidate.

The second case is that, in the index structure which is stored by theindex structure storing unit 13, at least a head portion of akana-reading of concatenated plural and consecutive words in a characterstring is identical with the inputted kana character string. In thiscase, on the condition that it is indicated that a head word out of theplural and consecutive words has the probability of being inputted firstat the time of inputting, the character string determining unit 11extracts a character string, which includes the plural and consecutivewords, from the index structure storing unit 13 and determines thecharacter string as the suggested candidate.

Next, a specific example of the index structure which the indexstructure storing unit 13 stores will be explained in the following withreference to FIG. 3 to FIG. 5. FIG. 3 is a diagram showing an example ofinformation which is recorded in the index structure used in the firstexemplary embodiment of the present invention. Each of FIG. 4A and FIG.4B is a diagram showing an example of the index structure used in thefirst exemplary embodiment of the present invention. FIG. 5 is a diagramshowing another example of the index structure used in the firstexemplary embodiment of the present invention.

As shown in FIG. 3, a plurality of words are recorded in the indexstructure which the index structure storing unit 13 stores, andfurthermore a kana-reading of each word is recorded. As mentioned above,not only the word which has the understandable mean by itself but alsothe incomplete word are recorded. Moreover, an item of ‘First’ in FIG. 3indicates whether or not it is probable that each word is inputted firstat the time of inputting. In the case that the probability is high, amark ‘∘’ is written in the item, and in the case that the probability islow, a mark ‘×’ is written in the item.

Furthermore, in the first exemplary embodiment, the word which isrecorded in the index structure is generated by dividing a candidate forthe character string to be suggested (refer to second exemplaryembodiment mentioned later). Specifically, ‘Af’ and ‘ghan’ are generatedby dividing ‘Afghan’. ‘N’, ‘E’, ‘C’,

and

are generated by dividing ‘NEC

.

,

,

,

and

are generated by dividing

.

and

are generated by dividing

.

,

, and

are generated by dividing

.

(Note 5) ‘NEC

is an abbreviation of ’‘NEC

, and means ‘NEC central research laboratory’ where ‘NEC’,

and

mean a company's name, ‘central’ and ‘research laboratory’ respectively.Kana-readings of ‘N’, ‘E’, ‘C’,

,

,

,

and

are

,

,

,

, and

respectively.

(Note 6)

means coccus poisoning where

and

mean ‘coccus’ and ‘poisoning’. Kana-readings of

and

are

and

respectively.

(Note 7)

means ‘atmizer for perfume’ where

and

mean ‘atmizer’, ‘for’, and ‘perfume’ respectively. Kana-readings of

and

are

and

respectively.

Moreover, for example, a candidate of

is divided into four words of

,

and

as mentioned above. Since it is conceivable that

and

out of the four words have the low probability of being inputted first,the mark ‘×’ is written in the item ‘First’. On the other hand, since itis conceivable that

and

have the high probability of being inputted first, the mark ‘∘’ iswritten in the item ‘First’.

Similarly, a candidate of

is divided into three words of

and

. Since it is conceivable that

out of the three words has the low probability of being inputted first,the mark ‘×’ is written in the item ‘First’. On the other hand, since itis conceivable that

and

have the high probability of being inputted first, the mark ‘∘’ iswritten in the item ‘First’.

An example of the index structure will be explained in the following. Inthe first exemplary embodiment, a case of carrying out the inputassistance by use of the character string (index structure) to besuggested shown in FIG. 4. As shown in FIG. 4A, ‘NEC

, ‘Afghan

, and

are recorded as a first pointer, a second pointer, a third pointer, afourth pointer and a fifth pointer respectively.

Moreover, as shown in FIG. 4B, the index structure is realized, forexample, by the try tree in the first exemplary embodiment. The trytree, which is a kind of the tree structure, is structured so that apath composing the tree structure may express a character string.Furthermore, in the try tree, a word having the probability of beinginputted first (word for which the mark ‘∘’ is written in the item‘First’ shown in FIG. 3) is used as an upper layer path, and the lowestlayer path is assigned a pointer which points a related candidate(character string to be suggested). Here, the upper layer means a leftside portion of FIG. 4B (FIG. 5) and the lower layer means a right sideportion of FIG. 4B (FIG. 5).

Accordingly, in the case that the index structure shown in FIG. 4B isused, the character string determining unit 11 traces the try tree onthe basis of the kana character inputted by the user, and, if an endnode at which the character string determining unit 11 arrives isassigned the pointer (corresponding to an item ‘Pointer’ in FIG. 4A),the character string determining unit 11 acquires a candidate (characterstring to be suggested) on the basis of the pointer. The acquiredcandidate is presented as the suggested candidate.

For example, it is assumed that the user inputs

as the kana character string. In this case, the character stringdetermining unit 11 firstly passes a path which is related to the kanacharacter string of

, and then acquires

, which is recorded as the fifth pointer, as the candidate. Next, thecharacter string determining unit 11 passes also a path which is relatedto the kana character string of

, and then acquires

, which is recorded as the fourth pointer, as the candidate.Accordingly, the character string determining unit 11 acquires twocandidates in this case.

Moreover, according to the first exemplary embodiment, as shown in FIG.5, a score may be set to each candidate for the character string to besuggested on the basis of statistical data (for example, appearancefrequency of each candidate in a source from which each candidate isacquired) in the index structure storing unit 13. In this case, whenthere are a plurality of the character strings to be suggested, thesuggested candidate presenting unit 12 can set an order of priority toeach character string on the basis of the score, and can present eachcharacter string as the suggested candidate in the order of priority.

Specifically, for example, it is assumed that the user inputs

as the kana character string. In this case, since the character stringdetermining unit 11 acquires

and

, the suggested candidate presenting unit 12 presents both as thesuggested candidate. According to the example shown in FIG. 5, thelatter candidate has the high score. Accordingly, the suggestedcandidate presenting unit 12 presents

and

in this order.

A method for determining the score is not limited. For example, thescore may be set so that a candidate, whose head portion is identicalwith the kana character string inputted by the user, may have a higherscore than a candidate, whose middle portion is identical with the kanacharacter string inputted by the user, has.

As mentioned above, in the case that, in the index structure, the scoreis set to each candidate for the character string to be suggested, acandidate which is more appropriate as the suggested candidate is listedat an upper position. As a result, accuracy in suggestion is improvedmore.

Moreover, according to the first exemplary embodiment, the suggestedcandidate presenting unit 12 can present a portion, which is included bythe character string to be suggested (that is, suggested candidate) thatis determined by the determining unit 11, and which is related to theinputted kana character string, in a form different from a form ofanother portion.

Specifically, in the case of using the form, the character stringdetermination unit 11 provides the suggested candidate presenting unit12 with not only the character string to be suggested but alsoinformation indicating where the portion, which is related to theinputted kana character string, exists in the character string to besuggested. Then, the suggested candidate presenting unit 12 identifiesthe portion, which is related to the inputted kana character string, onthe basis of the provided information, and carries out a highlightdisplay, addition of an under line, a change of font, a change of coloror the like to the identified portion.

Moreover, it is possible to identify the portion, for example, bysegments or marks which are assigned an starting point and an end pointof the portion related to the inputted kana character string.

Furthermore, if at least a head portion of a kana-reading of a word isidentical with the kana character string inputted by the user, the wordis exemplified as ‘portion related to the kana character string inputtedby the user’. Moreover, in the case that at least a head portion of akana-reading of concatenated plural and consecutive words in a characterstring is identical with the kana character string inputted by the user,the concatenated plural and consecutive words in the character stringare exemplified as ‘portion related to the kana character stringinputted by the user’,

For example, it is assumed that, in the case that the index structureshown in FIG. 3 is used, the user inputs

as the kana character string. In this case, the character stringdetermining unit 11 suggests

and simultaneously the suggested candidate presenting unit 12 carriesout the highlight display to

, or the like. Moreover, it is assumed that the user inputs

as the kana character string. Also in this case, the character stringdetermining unit 11 suggests

but, in this case, the suggested candidate presenting unit 12 carriesout the highlight display or the like to

,

.

According to the above-mentioned aspect, the user can quickly understandwhich portion in the character string, that is the suggested candidate,the kana character string inputted by the user is related to, and canquickly judge whether or not the character string which is the suggestedcandidate is appropriate. The aspect is useful at a time when the userdistinguishes a character string, which the user wants to input, among aplurality of suggested candidates.

[Operation of Device]

Next, an operation of the input assistance device 10 in the firstexemplary embodiment of the present invention will be explained withreference to FIG. 6. FIG. 6 is a flow diagram showing the operation ofthe input assistance device in the first exemplary embodiment of thepresent invention. In the following explanation, FIG. 2 to FIG. 5 areappropriately taken into consideration. Moreover, in the first exemplaryembodiment, by making the input assistance device 10 operate, an inputassistance method is carried out. Accordingly, to explain the operationof the input assistance device 10, which will be shown in the following,is identical with to explain an input assistance method in the firstexemplary embodiment.

As shown in FIG. 6, firstly, the character string determining unit 11 ofthe input assistance device 10 receives a character string which theuser inputs through the input unit 21 (Step A1). Next, the characterstring determining unit 11 judges whether or not the inputted characterstring is the kana character string (Step A2).

In the case that the judgment result in Step A2 is that the inputtedcharacteristic string is not the kana character string, a process in theinput assistance device 10 is ended. On the other hand, in the case thatthe judgment result in Step A2 is that the inputted characteristicstring is the kana character string, the character string determiningunit 11 acquires a character string, which is related to the inputtedkana character string and which includes a word having the probabilityof being inputted first at the time of inputting, from the indexstructure storing unit 13 as the character string to be suggested.

Specifically, in Step A3, the character string determining unit 11determines the character string to be suggested by comparing theinputted kana character string with the index structure shown in FIG. 3.Here, in the case that a head portion of the inputted kana characterstring is not identical with any one of the kana characters which arerecorded in the index structure storing unit 13, the process in theinput assistance device 10 is ended.

Next, the suggested candidate presenting unit 12 presents the characterstring, which is determined in Step A3, as the suggested candidate (StepA4). Specifically, the suggested candidate presenting unit 12 providesthe display unit 22 with the character string which is the suggestedcandidate. By carrying out the above, the user can confirm the suggestedcandidate which is related to the inputted kana character string, andafterward can determine the character string which the user searchesfor.

[Program]

Moreover, it is sufficient that a program in the first exemplaryembodiment is a program which makes a computer carry out Steps A1 to A4shown in FIG. 6. By installing the program in the computer and executingthe program, it is possible to realize the input assistance device 10and the input assistance method in the first exemplary embodiment. Inthis case, CPU (Central Processing Unit) of the computer works as thecharacter string determining unit 11 and the suggested candidatepresenting unit 12 to carry out the processes of these units.

[Effect of First Exemplary Embodiment]

As mentioned above, according to the first exemplary embodiment, only inthe case that the word, which is related to the kana character stringinputted by the user, has the probability of being inputted first, theinput assistance device can suggest the character string which includesthe word related to the kana character string. As a result, it ispossible to realize the input assistance with high accuracy insuggestion.

(Second Exemplary Embodiment)

Next, an input assistance device, an input assistance method and astorage device in a second exemplary embodiment of the present inventionwill be explained with reference to FIG. 7 to FIG. 9.

[Configuration of Device]

Firstly, a configuration of the input assistance device in the secondexemplary embodiment of the present invention will be explained withreference to FIG. 7. FIG. 7 is a block diagram showing a schematicconfiguration of the input assistance device in the second exemplaryembodiment of the present invention.

As shown in FIG. 7, an input assistance device 30 in the secondexemplary embodiment includes an index structure constructing unit 31differently from the input assistance device 10 in the first exemplaryembodiment.

Except for the above-mentioned point, the input assistance device 30 isconfigured similarly to the input assistance device 10 in the firstexemplary embodiment. Accordingly, with mainly focusing on the differentpoint, explanation will be given in the following.

Firstly, by use of a dictionary in which description of a word and itskana-reading is associated each other, the index structure constructingunit 31 divides a candidate for a character string to be suggested intoa plurality of partial character strings. ‘Partial character string’which is acquired by the division may be composed of only one word, andnumber of words has no limitation.

Next, the index structure constructing unit 31 judges whether or not itis probable that each of the acquired partial character strings isinputted first at a time of inputting the kana character string. At thistime, whether or not it is probable that each of the acquired partialcharacter strings is inputted first may be judged on the basis ofstatistical data of each partial character string, or on the basis of apredetermined rule.

Afterward, on the basis of the judgment result, the index structureconstructing unit 31 constructs index structure in which a kana-readingof the partial character string is defined as a key. Specifically, theindex structure constructing unit 31 constructs the try tree so that thekana-reading of the partial character string, which is judged to havethe probability of being inputted first, may be positioned at an upperlayer. The constructed try tree is stored by the index structure storingunit 13.

Moreover, in the second exemplary embodiment, the index structureconstructing unit 31 can set a score to each candidate for the characterstring to be suggested so that the score may become high as appearancefrequency in a set of documents including each candidate becomes high.Then, according to the aspect, in the case that there are a plurality ofthe character strings to be suggested, the suggested candidatepresenting unit 12 sets an order of priority to each character string tobe suggested on the basis of the score, and presents each characterstring as the suggested candidate in the order of priority.

Here, a function of the index structure constructing unit 31 will beexplained more specifically with reference to FIG. 8. FIG. 8 is adiagram showing an example of the dictionary which is used forconstructing the index structure in the second exemplary embodiment ofthe present invention.

Firstly, it is assumed that a list of candidates for the characterstring to be suggested is given through the computer 20. For example, itis assumed that the character strings of ‘Afghan’, ‘NEC

,

,

and

, which are mentioned in the first exemplary embodiment, are recorded inthe list. As mentioned above, these character strings can be acquiredfrom the input log, the product catalog, the address book, or the likewhich has been stored by the search system.

By use of the dictionary shown in FIG. 8, the index structureconstructing unit 31 divides each character string, which is recorded inthe list, into a plurality of partial character strings, and estimates akana-reading of each partial character string by use of thekana-readings which are recorded in the dictionary. As shown in FIG. 8,the word and its kana-reading are associated each other and theassociation is recorded in the dictionary. Moreover, not only a wordwhich is understandable by itself but also a word (incomplete word)which is generated by dividing the word understandable by itself arerecorded in the dictionary. Therefore, it is expected to improveaccuracy in estimating the kana-reading.

For example, it is assumed that the character string of ‘NEC

is recorded in the list. In the character string, ‘NEC’ is a name ofcompany, and

is an abbreviation of

and is used in the company. That is, both words are not general words.Accordingly, there is a high probability that the both words are notrecorded in a general Japanese language dictionary. As a result, in thecase of carrying out the division by use of the general Japaneselanguage dictionary, a situation that it is impossible to estimate thekana-reading is caused, and consequently a situation that ‘NEC

is not recorded in the index structure storing unit 13 as the characterstring to be suggested is caused. As mentioned above, in order to avoidthe above-mentioned situations, also the incomplete word is recorded inthe dictionary in the second exemplary embodiment.

The word and its kana-reading are associated each other and theassociation is recorded in the dictionary shown in FIG. 8 like ‘N(kana-reading:

)’, ‘E (kana-reading:

)’, ‘C (kana-reading:

)’,

(kana-reading:

)’ and

(kana-reading:

)’. Accordingly, it is possible to estimate the kana-reading of the wordwhich is not general like ‘NEC

, and the not-general word and its kana-reading are recorded by theindex structure storing unit 13.

Moreover, in the second exemplary embodiment, the word which is recordedin the dictionary may be composed of only one character or may becomposed of plural characters. Furthermore, the word which is recordedin the dictionary may be each of syllables into which a word in Englishis divided. For example, the word which is recorded in the dictionarymay be each of ‘Af’ (kana-reading:

)’ and ‘ghan (kana-reading:

)’ which are generated by dividing ‘Afghan (kana-reading:

)’.

Moreover, as shown in FIG. 8, an item of ‘Long content word’ is set inthe third column of the dictionary from the left. By use of the mark ‘∘’or ‘×’, the item indicates whether or not each word is composed ofcharacters whose number is equal to or larger than a predeterminednumber (for example, 2). In the case that the word is composed ofcharacters whose number is equal to or larger than the predeterminednumber, the mark ‘∘’ is written in the item, and in the case that theword is composed of characters whose number is equal to or smaller thanthe predetermined number, the mark ‘×’ is written in the item. Here, inthe case that the word is composed of characters whose number is equalto the predetermined number, whether the mark ‘∘’ or ‘×’ is written inthe item is within discretion of a designer. Here, since a wordincluding many characters is apt to be inputted first at the time ofinputting, information recorded in the item of ‘Long content word’ maybe used in a process of judging ‘whether or not it is probable to beinputted first’. Here, the process will be mentioned later.

Furthermore, in the example shown in FIG. 8, ‘A part of speech’ of eachword may be recorded in the dictionary as shown in a fourth column ofthe dictionary from the left. Moreover, information which indicates akind of character such as the hiragana and the kanji may be recorded asshown in a fifth column from the left.

Then, in the case that the character string of ‘NEC

is recorded in the list, as mentioned above, the index structureconstructing unit 31 divides the character string into five partialcharacter strings by use of ‘N’, ‘E’, ‘C’,

and

which are recorded in the dictionary shown in FIG. 8. Moreover, in thecase that the character string of

is recorded in the list, the index structure constructing unit 31divides the character string into four partial character strings by useof

,

,

, and

which are recorded in the dictionary shown in FIG. 8. Moreover, in thecase that the character string of ‘Afghan’ is recorded in the list, theindex structure constructing unit 31 divides the character string intotwo partial character strings by use of ‘Af’ and ‘ghan’ which arerecorded in the dictionary shown in FIG. 8.

Moreover, in the second exemplary embodiment, the Viterbi algorithm orthe like, which is conventionally used in a filed of the morphemicanalysis, can be used in the process of dividing the character stringinto the partial character strings. Moreover, in the division process,normalization of the character string such as conversion of thesingle-space character into the full-size character may be carried out.

Next, the index structure constructing unit 31 judges whether or not itis probable that each partial character string is inputted first at thetime of inputting the kana character string. In this case, the indexstructure constructing unit 31 can judge the probability, for example,on the basis of statistical data of the partial character string.

For example, if the candidate for the character string to be suggestedis acquired from the past input log which the search system accumulates,the index structure constructing unit 31 uses appearance frequency ofthe input log including the partial character string, which is a subjectfor the judgment process, as the statistical data. In this case,firstly, the index structure constructing unit 31 generates a characterstring by concatenating the partial character string, which is thesubject of the judgment process, with a character string which followsthe partial character string. A character string which includes a headcharacter to a N'th character of the generated character string is asubject of measurement. If the following character string does notexist, a character string which includes a head character to a N'thcharacter of the partial character string is the subject of measurement.

Then, the index structure constructing unit 31 measures the appearancefrequency of the character string, which is the subject of measurement,in the past input logs which the search system accumulates, and judgesthat it is probable that the partial character string is inputted firstwhen the measured appearance frequency is higher than a predeterminedlevel. For example, in the case that N is 2, when there are many inputlogs whose head word is

and there are few input logs whose head word is

, it is judged that it is probable that a partial character string whosehead word is

is inputted first, and it is judged that it is not probable that apartial character string whose head word is

is inputted first.

Moreover, the index structure constructing unit 31 can set a score,which expresses aptness to be inputted first, to each candidate for thecharacter string to be suggested by use of the measured appearancefrequency. In this case, similarly to the first exemplary embodiment,when there are a plurality of the character strings to be suggested, thesuggested candidate presenting unit 12 can set an order of priority toeach character string on the basis of the score, and can present eachcharacter string as the suggested candidate in the order of priority.

Moreover, the statistical data may be acquired from text data other thanthe input log, for example, from a Web page. Specifically, by using acomma and a period or by use of the morphemic analysis, the indexstructure constructing unit 31 firstly divides text data into asentence, a clause or a word.

Next, when the division is completed, the index structure constructingunit 31 generates a character string by concatenating the partialcharacter string, which is the subject of the judgment process, with acharacter string which follows the partial character string. Then, theindex structure constructing unit 31 measures number of times when acharacter string including a head character to a N'th character of thegenerated character string is used at a head of the sentence, the clauseor the word, and judges that it is probable that the partial characterstring is inputted first when the measured number of times is higherthan a predetermined number. If the following character string does notexist, a character string which includes a head character to a N'thcharacter of the partial character string is the subject of measurement.

Moreover, the judgment process carried out by the index structureconstructing unit 31 may be carried out on the basis of a predeterminedrule. Or, a combination of plural rules may be used. For example, thereis a case that, in a document, there is a switch point where a kind ofcharacter switches like a switch point where a kind of characterswitches from the kanji to the alphabet, or the like. In this case, itis possible to judge that it is probable that a partial characterstring, which appears at the rear of the switch point, is inputtedfirst. By prescribing the above as a rule, it is possible to carry outthe above-mentioned judgment process.

Moreover, the judgment process can be carried out by setting a contentword to the dictionary in advance. For example, the judgment process maybe carried out on the basis of the marks ‘∘: long’ and ‘×: short’ whichare written in the item of ‘Long content word’. In this case, it isjudged that it is probable that a partial character string, whose headportion is identical with the long content word, is inputted first.Furthermore, the judgment process can be carried out on the basis of thepart of speech of the word which is recorded in the dictionary. Forexample, by judging that it is probable that Noun and Verb are inputtedfirst and it is not probable that Postpositional particle and Modal verbare inputted first, the judgment process may be carried out.

Specifically, by carrying out any one of the above-mentioned judgmentprocesses, the index structure constructing unit 31 judges that, forexample, in the case of the character string of ‘NEC

, it is probable that ‘N’ and

are inputted first, and it is not probable that ‘E’, ‘C’ and

are inputted first.

Then, similarly to the first exemplary embodiment, the index structureconstructing unit 31 constructs the index structure by defining akana-reading of the partial character string, which is judged to havethe probability of being inputted first, as a key. For example, in theexample of ‘NEC

, the index structure constructing unit 31 constructs a path in which

(N) is at an upper layer position, and a path in which

(

) is at an upper layer position. Moreover, the index structureconstructing unit 31 connects

(E:

is macron for

),

(C),

(

) and

(

), which follow

, with

in this order in the path in which

is at the upper position, and connects

with

in the path in which

is at the upper position.

In the case that the user inputs

or

, ‘NEC

is suggested by using the index structure which is constructed asmentioned above. However, in the case that the user inputs

or the like, since it is judged that it is not probable that the kanacharacter string is inputted first, ‘NEC

is not suggested

Moreover, the index structure constructing unit 31 can record a segment,which clearly indicates a boundary between the partial character stringsin order to identify each partial character string acquired by thedivision by use of the dictionary, in the index structure. As mentionedin the first exemplary embodiment, the segment is useful in the casethat the character string determining unit 11 generates informationindicating which portion is related to the inputted kana characterstring, and the segment makes realization of the highlight display orthe like easy.

[Operation of Device]

Next, an operation of the input assistance device 30 in the secondexemplary embodiment of the present invention will be explained withreference to FIG. 9. FIG. 9 is a flow diagram showing the operation ofthe input assistance device in the second exemplary embodiment of thepresent invention. In the following explanation, FIG. 7 and FIG. 8 aretaken into consideration.

Moreover, in the second exemplary embodiment, by making the inputassistance device 30 operate, an input assistance method is carried out.Accordingly, to explain the input assistance method in the secondexemplary embodiment is also to explain the operation of the inputassistance device 30 which will be shown in the following. Here, since aprocess other than the construction process of the index structure isthe same as one in the first exemplary embodiment, the constructionprocess of the index structure will be explained in the following.

As shown in FIG. 9, the index structure constructing unit 31 firstlyacquires the candidate for the character string to be suggested (StepB1). Specifically, the candidate is acquired from an input log, aproduct catalog, an address book, or the like which has been stored by asearch system.

Next, by use of the dictionary show in FIG. 8, the index structureconstructing unit 3 divides each character string, which is recorded inthe list, into a plurality of partial character strings and generatesthe partial character strings (Step B2). Next, by using the kana-readingwhich is recorded in the dictionary, the index structure constructingunit 31 estimates the kana-reading of each partial character stringwhich is generated in Step B2 (Step B3).

Next, the index structure constructing unit 3 judges whether or not itis probable that each character string is inputted first at the timingof inputting the kana character string (Step B4). The judgment processin Step B4 is carried out on the basis of statistical data, apredetermined rule, information recorded in the dictionary, or the like.

Afterward, by use of the judgment result in Step B4, the index structureconstructing unit 31 constructs a plurality of paths so that thekana-reading of the partial character string, which is judged to havethe probability of being inputted first, may be defined as the key, andthen, similarly to the first exemplary embodiment, the index structureconstructing unit 3 constructs the index structure (Step B5). Theconstructed index structure is stored by the index structure storingunit 13.

[Program]

Moreover, it is sufficient that a program in the second exemplaryembodiment is a program which makes a computer carry out Steps A1 to A4shown in FIG. 6, and Steps B1 to B5 shown in FIG. 9. By installing theprogram in the computer and executing the program, it is possible torealize the input assistance device 30 and the input assistance methodin the second exemplary embodiment. In this case, CPU of the computerworks as the character string determining unit 11, the suggestedcandidate presenting unit 12 and the index structure constructing unit31 and carries out the processes of these units.

[Effect in Second Exemplary Embodiment]

As mentioned above, according to the second exemplary embodiment, theinput assistance device 30 constructs the index structure by itself. Asa result, a manager of the input assistance device 30 may merely inputthe input log, the product catalog, the address book or the like, whichhas been stored by the search system, into the device. Moreover, also inthe case of using the second exemplary embodiment, it is possible toacquire the effect which is mentioned in the first exemplary embodiment.

(Third Exemplary Embodiment)

Next, a configuration of an input assistance device in a third exemplaryembodiment of the present invention will be explained. FIG. 11 is ablock diagram showing a schematic configuration of an input assistancedevice 10 a in the third exemplary embodiment of the present invention.The input assistance device 10 a in the third exemplary embodiment is adevice for assisting a user to input a character string, and includes acharacter string determining unit 11 a and a suggested candidatepresenting unit 12 a.

In the case that the user inputs a kana character string, the characterstring determining unit 11 a determines a character string to besuggested, which is related to the inputted kana character string, byuse of index structure in which a word and its kana-reading are recordedand which indicates whether or not it is probable that the word isinputted first at a time of inputting a character string. The suggestedcandidate presenting unit 12 a presents the character string to besuggested, which is determined by the character string determining unit11 a, as a suggested candidate.

According to the third exemplary embodiment of the present invention, inconsideration of the probability of being inputted first in thecharacter string, the input assistance device suggests the characterstring to be suggested to the user. As a result, it is possible to carryout input assistance with high accuracy in suggestion.

Here, a computer, which realizes the input assistance device by carryingout the programs in the first to the third exemplary embodimentsmentioned above, will be explained with reference to FIG. 10. FIG. 10 isa block diagram showing an example of the computer which realizes theinput assistance devices in the first and the second exemplaryembodiments of the present invention.

As shown in FIG. 10, a computer 20 includes CPU 111, a main memory 112,a storage device 113, an input interface 114, a display controller 115,a data reader/writer 116 and a communication interface 117. Theabove-mentioned units are connected each other through a bus 121 so asto be able to carry out data communication.

CPU 111 expands the program (code) in the present exemplary embodiment,which is stored by the storage device 113, to the main memory 112, andcarries out various calculations by executing the program in apredetermined order. The main memory 112 is a volatile storage devicesuch as DRAM (Dynamic Random Access Memory) or the like as a typicalexample. Moreover, the program in the present exemplary embodiment isprovided in a state stored by a computer-readable record medium 120.Here, the program in the present exemplary embodiment may be a programwhich circulates on the Internet with which the computer 20 is connectedthrough the communication interface 117.

Moreover, as a specific example of the storage device 113, asemiconductor storage device such as a flash memory or the like isexemplified in addition to a hard disk. The input interface 114 mediatesdata transmission between CPU 111 and an input equipment 118 such as akey board and a mouse. The display controller 115 is connected with adisplay device 119 and controls display which is carried out by thedisplay device 119.

The data reader/writer 116 mediates data transmission between CPU 111and the record medium 120. That is, the data reader/writer 116 reads theprogram from the record medium 120, and writes a process result providedby the computer 20 into the record medium 120. The communicationinterface 117 mediates data transmission between CPU 111 and anothercomputer.

Moreover, as a specific example of the record medium 120, a generalsemiconductor storage device such as CF (Compact Flash), SD (SecureDigital) or the like, a magnetic storage medium such as Flexible Disk orthe like or an optical storage medium such as CD-ROM (Compact Disk ReadOnly Memory)or the like is exemplified.

Here, a computer which realizes the input assistance devices in thefirst to the third exemplary embodiments is not limited to the computershown in FIG. 10. The computer which realizes the input assistancedevices in the first to the third exemplary embodiments may be acomputer, which requires an input operation carried out by the user,such as a personal computer, Smartphone, a tablet type terminal device,a car navigation system or the like.

INDUSTRIAL APPLICABILITY

As mentioned above, according to the present invention, it is possibleto present the character string to be suggested to the user inconsideration of the probability of being inputted first in thecharacter string. The present invention is useful in a field requiringthe input operation carried out by the user, for example, a searchsystem, a word processor, or the like

The present invention has been explained by explaining theabove-mentioned exemplary embodiments as preferable examples. However,the present invention is not limited to the above-mentioned exemplaryembodiments. That is, the present invention can apply various aspects,which a person skilled in the art can understand, within the scope ofthe present invention.

This application is based upon and claims the benefit of priority fromJapanese Patent Application No. 2013-243128, filed on Nov. 25, 2013, thedisclosure of which is incorporated herein in its entirety by reference.

REFERENCE SIGNS LIST

10 input assistance device

11 character string determining unit

12 suggested candidate presenting unit

13 index structure storing unit

20 computer

21 input unit

22 display unit

30 input assistance device

31 index structure constructing unit

111 CPU

112 main memory

113 storage device

114 input interface

115 display controller

116 data reader/writer

117 communication interface

118 input equipment

119 display device

120 record medium

121 bus

What is claimed is:
 1. An input assistance device for assisting Englishcharacter input by a user, comprising: an index structure storage thatstores at least sets of information, wherein each set of the setsassociate a kana-reading of an English word, in which a character stringto be recommended to the user is divided, with a mark indicating whetheror not the English word is likely to be firstly inputted at the time ofthe English character input; character strings to be recommended to theuser; and an index structure that registers kana-readings of thecharacter strings to be recommended to the user; one or more processorsconfigured as a character string determining unit that determines acharacter string which includes a word of the sets to be suggested tothe user, in a case that one or more English characters inputted by theuser matches at least a part of a kana-reading of a character string tobe recommended to the user stored in the index structure storage; thepart matching is the kana-reading of the English word of the sets; andthe mark relating to the word of the sets indicates the word of the setsis likely to be firstly inputted at the time of the English characterinput, and the one or more processors are further configured as asuggested candidate presenting unit that presents the character stringto be suggested, which is determined, as a suggested candidate.
 2. Theinput assistance device according to claim 1, wherein the characterstring determining unit determines the character string which includesthe word of the sets to be suggested to the user, in a case that the oneor more English characters inputted by the user matches at least a headportion of the kana-reading of the character string to be recommended tothe user stored in the index structure storage, the part is thekana-reading of the English word of the sets, and the mark relating tothe word of the sets indicates the word of the sets is likely to befirstly inputted at the time of the English character input,furthermore, the character string determining unit determines acharacter string, which includes concatenating plural and consecutivewords of the sets to be suggested to the user, in a case that the one ormore English characters inputted by the user matches at least a headportion of a kana-reading of the concatenating plural and consecutivewords of the sets in the character string to be suggested to the users,and the mark relating to the head portion of the words of the setsindicates the head portion is likely to be firstly inputted at the timeof the English character input.
 3. The input assistance device accordingto claim 1, wherein the one or more processors are further configured asan index structure constructing unit that divides a candidate for thecharacter string to be suggested into a plurality of partial characterstrings, by use of a dictionary in which description of the English wordand a kana-reading of the English word are associated with each other,judges whether or not it is probable that the partial character stringis inputted first at the time of inputting the character string, per thepartial character string out of the plural character strings, andconstructs the index structure, in which a kana-reading of the partialcharacter string is defined as a key, based on a result of the judge. 4.The input assistance device according to claim 3, wherein on the basisof statistical data of the partial character string, the index structureconstructing unit judges whether or not it is probable that the partialcharacter string is inputted first at the time of inputting thecharacter string.
 5. The input assistance device according to claim 3,wherein on the basis of a predetermined rule, the index structureconstructing unit judges whether or not it is probable that the partialcharacter string is inputted first at the time of inputting thecharacter string.
 6. The input assistance device according to claim 4,wherein the index structure constructing unit further sets a score toeach of the candidates for the character string to be suggested, basedon the statistical data, and the suggested candidate presenting unitsets a priority order to each of the character strings to be suggestedon the basis of the score, and presents the character strings as thesuggested candidate in the priority order, in the case that there are aplurality of the character strings to be suggested which are determined.7. The input assistance device according to claim 1, wherein thesuggested candidate presenting unit presents a portion, which isincluded by the character string to be suggested that is determined andwhich is related to the inputted character string, in a form differentfrom another portion form.
 8. An input assistance method for assistingEnglish character input by a user by use of a computer, the methodcomprising: storing, with an index structure storage, at least sets ofinformation, wherein each set of the sets associate a kana-reading ofEnglish word, in which a character string to be recommended to the useris divided, with a mark indicating whether or not the word is likely tobe firstly inputted at the time of the character input; characterstrings to be recommended to the user; and an index structure thatregisters kana-readings of the character strings to be recommended tothe user; determining, with one or more processors, a character stringwhich includes a word of the sets to be suggested to the user, in a casethat one or more English characters inputted by the user matches atleast a part of a kana-reading of a character string to be recommendedto the user stored in the index structure storage; the part matching isthe kana-reading of the word of the sets; and the mark relating to theword of the sets indicates the word of the sets is likely to be firstlyinputted at the time of the character input, and presenting, with theone or more processors, the character string to be suggested, which isdetermined, as a suggested candidate.
 9. The input assistance methodaccording to claim 8, wherein in the determining, determining acharacter string, which includes the word of the sets to be suggested tothe user, in a case that the one or more English characters inputted bythe user matches at least a head portion of the kana-reading of thecharacter string to be recommended to the user stored in the indexstructure storage, the part is the kana-reading of the English word ofthe sets, and the mark relating to the English word of the setsindicates the English word of the sets is likely to be firstly inputtedat the time of the English character input, furthermore, in thedetermining, determining the character string, which includesconcatenating plural and consecutive English words of the sets to besuggested to the user, in a case that the one or more English charactersinputted by the user matches at least a head portion of a kana-readingof the concatenating plural and consecutive words of the sets in thecharacter string to be suggested to the users, and the mark relating tothe head portion of the words of the sets indicates the head portion islikely to be firstly inputted at the time of the English characterinput.
 10. The input assistance method according to claim 8, furthercomprising processing of constructing an index structure, whereindividing a candidate for the character string to be suggested into aplurality of partial character strings, by use of a dictionary in whichdescription of the English word and a kana-reading of the English wordare associated with each other, judging whether or not it is probablethat the partial character string is inputted first at the time ofinputting the character string, per the partial character string out ofthe plural character strings, and constructing the index structure, inwhich a kana-reading of the partial character string is defined as akey, based on a result of the judge.
 11. The input assistance methodaccording to claim 10, wherein in the constructing, judging whether ornot it is probable that the partial character string is inputted firstat the time of inputting the character string based on statistical dataof the partial character string.
 12. The input assistance methodaccording to claim 10, wherein in the constructing, based on apredetermined rule, judging whether or not it is probable that thepartial character string is inputted first at the time of inputting thecharacter string.
 13. A non-transitory computer readable storage mediumfor storing a program to assist English character input by a user by acomputer, the program cause the computer implement for: storing, with anindex structure storage, at least sets of information, wherein each setof the sets associate a kana-reading of an English word, in which acharacter string to be recommended to the user is divided, with a markindicating whether or not the word is likely to be firstly inputted atthe time of the character input; character strings to be recommended tothe user; and an index structure that registers kana-readings of thecharacter strings to be recommended to the user; determining, with oneor more processors, a character string which includes a word of the setsto be suggested to the user, in a case that one or more Englishcharacters inputted by the user matches at least a part of akana-reading of a character string to be recommended to the user storedin the index structure storage; the part matching is the kana-reading ofthe English word of the sets; and the mark relating to the word of thesets indicates the word of the sets is likely to be firstly inputted atthe time of the character input, and presenting the character string tobe suggested, which is determined, as a suggested candidate.
 14. Thestorage medium according to claim 13, wherein in the determining,determining a character string, which includes the word of the sets tobe suggested to the user, in a case that the one or more Englishcharacters inputted by the user matches at least a head portion of thekana-reading of the character string to be recommended to the userstored in the index structure storage, the part is the kana-reading ofthe English word of the sets, and the mark relating to the English wordof the sets indicates the English word of the sets is likely to befirstly inputted at the time of the English character input,furthermore, in the determining, determining the character string, whichincludes concatenating plural and consecutive English words of the setsto be suggested to the user, in a case that the one or more Englishcharacters inputted by the user matches at least a head portion of akana-reading of the concatenating plural and consecutive words of thesets in the character string to be suggested to the users, and the markrelating to the head portion of the words of the sets indicates the headportion is likely to be firstly inputted at the time of the Englishcharacter input.
 15. storage medium according to claim 13, furthercomprising processing of constructing an index structure, whereindividing a candidate for the character string to be suggested into aplurality of partial character strings, by use of a dictionary in whichdescription of the English word and a kana-reading of the English wordare associated with each other, judging whether or not it is probablethat the partial character string is inputted first at the time ofinputting the character string, per the partial character string out ofthe plural character strings, and constructing the index structure, inwhich a kana-reading of the partial character string is defined as akey, based on a result of the judge.
 16. The storage medium according toclaim 15, wherein in the constructing, judging whether or not it isprobable that the partial character string is inputted first at the timeof inputting the character string based on statistical data of thepartial character string.
 17. The storage medium according to claim 15,wherein in the constructing, based on a predetermined rule, judgingwhether or not it is probable that the partial character string isinputted first at the time of inputting the character string.